Natural resources conservation management and strategies in agriculture

This paper suggests a holistic framework for assessment and improvement of management strategies for conservation of natural resources in agriculture. First, it incorporates an interdisciplinary approach (combining Economics, Organization, Law, Sociology, Ecology, Technology, Behavioral and Political Sciences) and presents a modern framework for assessing environmental management and strategies in agriculture including: specification of specific “managerial needs” and spectrum of feasible governance modes (institutional environment; private, collective, market, and public modes) of natural resources conservation at different level of decision-making (individual, farm, eco-system, local, regional, national, transnational, and global); specification of critical socio-economic, natural, technological, behavioral etc. factors of managerial choice, and feasible spectrum of (private, collective, public, international) managerial strategies; assessment of efficiency of diverse management strategies in terms of their potential to protect diverse eco-rights and investments, assure socially desirable level of environmental protection and improvement, minimize overall (implementing, third-party, transaction etc.) costs, coordinate and stimulate eco-activities, meet preferences and reconcile conflicts of individuals etc. Second, it presents evolution and assesses the efficiency of diverse management forms and strategies for conservation of natural resources in Bulgarian agriculture during post-communist transformation and EU integration (institutional, market, private, and public), and evaluates the impacts of EU CAP on environmental sustainability of farms of different juridical type, size, specialization and location. Finally, it suggests recommendations for improvement of public policies, strategies and modes of intervention, and private and collective strategies and actions for effective environmental protection

Optimized R functions for analysis of ecological community data using the R virtual laboratory (RvLab)

Background: Parallel data manipulation using R has previously been addressed by members of the R community, however most of these studies produce ad hoc solutions that are not readily available to the average R user. Our targeted users, ranging from the expert ecologist/microbiologists to computational biologists, often experience difficulties in finding optimal ways to exploit the full capacity of their computational resources. In addition, improving performance of commonly used R scripts becomes increasingly difficult especially with large datasets. Furthermore, the implementations described here can be of significant interest to expert bioinformaticians or R developers. Therefore, our goals can be summarized as: (i) description of a complete methodology for the analysis of large datasets by combining capabilities of diverse R packages, (ii) presentation of their application through a virtual R laboratory (RvLab) that makes execution of complex functions and visualization of results easy and readily available to the end-user. New information: In this paper, the novelty stems from implementations of parallel methodologies which rely on the processing of data on different levels of abstraction and the availability of these processes through an integrated portal. Parallel implementation R packages, such as the pbdMPI (Programming with Big Data – Interface to MPI) package, are used to implement Single Program Multiple Data (SPMD) parallelization on primitive mathematical operations, allowing for interplay with functions of the vegan package. The dplyr and RPostgreSQL R packages are further integrated offering connections to dataframe like objects (databases) as secondary storage solutions whenever memory demands exceed available RAM resources. The RvLab is running on a PC cluster, using version 3.1.2 (2014-10-31) on a x86_64-pc-linux-gnu (64-bit) platform, and offers an intuitive virtual environmet interface enabling users to perform analysis of ecological and microbial communities based on optimized vegan functions. A beta version of the RvLab is available after registration at: https://portal.lifewatchgreece.eu

Scather: programming with multi-party computation and MapReduce

We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of supporting MapReduce [27] operations) in concert with multi-party computation (MPC) to execute statistical analysis algorithms that have privacy-preserving properties. Our architecture allows a data analyst unfamiliar with MPC to: (1) author an analysis algorithm that is agnostic with regard to data privacy policies, (2) to use an automated process to derive algorithm implementation variants that have different privacy and performance properties, and (3) to compile those implementation variants so that they can be deployed on an infrastructures that allows computations to take place locally within each participant’s MapReduce cluster as well as across all the participants’ clusters using an MPC protocol. We describe implementation details of the architecture, discuss and demonstrate how the formal framework enables the exploration of tradeoffs between the efficiency and privacy properties of an analysis algorithm, and present two example applications that illustrate how such an infrastructure can be utilized in practice.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798

TCG based approach for secure management of virtualized platforms: state-of-the-art

There is a strong trend shift in the favor of adopting virtualization to get business benefits. The provisioning of virtualized enterprise resources is one kind of many possible scenarios. Where virtualization promises clear advantages it also poses new security challenges which need to be addressed to gain stakeholders confidence in the dynamics of new environment. One important facet of these challenges is establishing 'Trust' which is a basic primitive for any viable business model. The Trusted computing group (TCG) offers technologies and mechanisms required to establish this trust in the target platforms. Moreover, TCG technologies enable protecting of sensitive data in rest and transit. This report explores the applicability of relevant TCG concepts to virtualize enterprise resources securely for provisioning, establish trust in the target platforms and securely manage these virtualized Trusted Platforms